scholarly journals Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 46 ◽  
Author(s):  
I-Kuai Hung ◽  
Daniel Unger ◽  
David Kulhavy ◽  
Yanli Zhang
Keyword(s):  
Flight Control ◽  
Fundamental Question ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Ground Control ◽  
Image Mosaic ◽  
Control Points ◽  
Ground Control Points ◽  
Positional Precision

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight.

scholarly journals GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY

2020 ◽  
Vol 92,2020 (92) ◽  
pp. 15-23
Author(s):  
O. L., Dorozhynskyy ◽  
◽  
I. Z. Kolb ◽  
L. V. Babiy ◽  
L. V. Dychko ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Aerial Survey ◽  
Reference Points ◽  
Aerial Images ◽  
Aerial Image ◽  
Ground Control ◽  
Reference Image ◽  
Control Points ◽  
Ground Control Points ◽  
Reference Images

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

scholarly journals Archival Aerial Images Georeferencing: A Geostatistically-Based Approach for Improving Orthophoto Accuracy with Minimal Number of Ground Control Points

2020 ◽  
Vol 12 (14) ◽  
pp. 2232
Author(s):  
Manuela Persia ◽  
Emanuele Barca ◽  
Roberto Greco ◽  
Maria Immacolata Marzulli ◽  
Patrizia Tartarino
Keyword(s):  
Southern Italy ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Territorial Planning ◽  
Ground Control Points ◽  
Study Results ◽  
Open Issue ◽  
Historical Photographs

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

Development of a 3D Viewer for georeferencing and monoplotting of historical terrestrial images.

2020 ◽  
Author(s):  
Sebastian Flöry ◽  
Camillo Ressl ◽  
Gerhard Puercher ◽  
Norbert Pfeifer ◽  
Markus Hollaus ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Local Scale ◽  
Aerial Imagery ◽  
Ground Control ◽  
Control Points ◽  
Mountainous Regions ◽  
Terrain Model ◽  
Digital Terrain ◽  
Ground Control Points ◽  
Selection Of

<p>Mountain regions are disproportionately affected by global warming and changing precipitation conditions. Especially the strong variations within high mountain ranges at the local scale require additional sources in order to quantify changes within this challenging environment. With the emergence of alpine tourism, terrestrial photographs became available by the end of 1800, predating aerial imagery for the selected study areas by 50 years. Due to the earlier availability and oblique acquisition geometry these images are a promising source for quantifying changes within mountainous regions at the local scale. Within the research project SEHAG, methods to process these images and to analyse their potential to quantify and describe environmental changes are developed and applied to study areas in Austria and Italy.</p><p>One of the prerequisites for the estimation of changes based on terrestrial imagery is the calculation of the corresponding object point for each pixel in a global coordinate system resulting in a georeferenced orthorectified image. This can be achieved by intersecting the ray defined by the projection center of the camera and each pixel with a digital terrain model, a process known as monoplotting.</p><p>So far 1000 terrestrial images with unknown interior and exterior orientation have been collected from various archives for the selected study areas Kaunertal, Horlachtal (both Tyrol, Austria) and Martelltal (South Tyrol, Italy). In order to estimate all camera parameters a 3D viewer for the selection of ground control points has been developed and implemented. The estimation of the exterior and interior orientation is done in OrientAL. </p><p>Preliminary results for selected images show, that especially the developed 3D viewer is an important improvement for the selection of well distributed ground control points and the accurate estimation of the exterior and interior orientation. Monoplotting depends on a digital terrain model, which cannot be computed from the terrestrial images alone due to missing overlap and different acquisitions times. Hence, the combination with historical digital terrain models derived from aerial imagery is necessary to minimize errors introduced due to changes in topography until today. While the large amount of terrestrial images with their oblique acquisition geometries can be exploited to fill occluded areas by combining the results from multiple images, the partly missing or inaccurate temporal information poses another limitation.</p><p>With this large image collection, for the first time, we are able to evaluate the use of historical oblique terrestrial photographs for change detection in a systematic manner. This will promote knowledge about challenges, limitations and the achievable accuracy of monoplotting within mountainous regions. The work is part of the SEHAG project (project number I 4062) funded by the Austrian Science Fund (FWF).</p>

scholarly journals THE IMPLEMENTATION OF A PHOTOGRAMMETRIC PROCEDURE FOR THE ADJUSTMENT OF THE OLD RASTER CADASTRAL PLANS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 177-182
Author(s):  
K. Si youcef ◽  
I. Boukerch ◽  
F. Z. Belhouari ◽  
A. M. Seddiki ◽  
B. Takarli
Keyword(s):  
Optimal Number ◽  
Aerial Images ◽  
Second Step ◽  
Ground Control ◽  
Optimal Distribution ◽  
Control Points ◽  
Large Area ◽  
Ground Control Points

Abstract. Algeria faces challenges of globalization. It classifies the establishment of the national general urban and rural territory cadastre as top priority. The National Cadastre Agency has implemented a policy aimed at improving the quality and accuracy of the resulting documentation, in order to widen the scope of the latter in the various fields.Since the launching of the first operations to establish the general cadastre of the national territory, the graphic cadastral documentation which was carried out based on aerial images (ortho-photographs or restitution plans) present mismatch either between the external borders or between the section plans that compose the communal (municipal) cadastral plane.This article describes one of the simultaneous plane adjustment techniques inspired by the aero triangulation used in photogrammetry. In a first step, we built the photogrammetric unit where we consider the cadastral planes as photogrammetric models. In a second step, the constructed units will be used to form a superstructure covering a very large area like in the photogrammetric block case. Finally, this superstructure is adjusted, where the discrepancies are reduces relatively between these section plans using Tie Points (TP) and absolutely by relying on an optimal number of Ground Control Points (GCP) in the terrain system suitably distributed on the block.This technique makes it possible to preserve the relationships between the data in a precise way and to guarantee the continuity in the acquisition of the data which can be added later. It also makes it possible to solve the problem of the overlap between the isolated section plans due to the non-optimal distribution, the insufficiency, or the absence of control points.The evaluation results obtained after the experiments report that the proposed adjustment technique is efficient to solve such a problem.

scholarly journals PANTIR - A DUAL CAMERA SETUP FOR PRECISE GEOREFERENCING AND MOSAICING OF THERMAL AERIAL IMAGES

2015 ◽  
Vol XL-3/W2 ◽  
pp. 269-272 ◽  
Author(s):  
I. Weber ◽  
A. Jenal ◽  
C. Kneer ◽  
J. Bongartz
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Spatial Resolution ◽  
Common Ground ◽  
Aerial Images ◽  
Ground Control ◽  
Thermal Activity ◽  
Control Points ◽  
High Quality ◽  
Ground Control Points

Research and monitoring in fields like hydrology and agriculture are applications of airborne thermal infrared (TIR) cameras, which suffer from low spatial resolution and low quality lenses. Common ground control points (GCPs), lacking thermal activity and being relatively small in size, cannot be used in TIR images. Precise georeferencing and mosaicing however is necessary for data analysis. Adding a high resolution visible light camera (VIS) with a high quality lens very close to the TIR camera, in the same stabilized rig, allows us to do accurate geoprocessing with standard GCPs after fusing both images (VIS+TIR) using standard image registration methods.

scholarly journals GEOREFERENCING UAS DERIVATIVES THROUGH POINT CLOUD REGISTRATION WITH ARCHIVED LIDAR DATASETS

2016 ◽  
Vol IV-2/W1 ◽  
pp. 195-199 ◽  
Author(s):  
M. S. L. Y. Magtalas ◽  
J. C. L. Aves ◽  
A. C. Blanco
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Model Errors ◽  
Validation Data ◽  
Positioning Systems ◽  
Ground Control Points ◽  
Skeleton Point

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a ‘skeleton point cloud’. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of CANUPO and manual skeleton clouds were obtained with values for both equal to around 0.67 meters at 1.73 standard deviation.

Assessing the Geospatial Accuracy of Aerial Imagery Collected with Various UAS Platforms

2018 ◽  
Vol 61 (6) ◽  
pp. 1823-1829 ◽  
Author(s):  
Alysa A. Gauci ◽  
Christian J. Brodbeck ◽  
Aurelie M. Poncet ◽  
Thorsten Knappenberger
Keyword(s):  
Data Processing ◽  
Precision Agriculture ◽  
Unmanned Aircraft ◽  
Aerial Imagery ◽  
Ground Control ◽  
Control Points ◽  
Flight Altitude ◽  
Gps Receivers ◽  
Ground Control Points ◽  
Very High

Abstract. Recent development of small unmanned aircraft systems (UAS) provides a relatively low-cost solution to collect aerial imagery with very high spatial and temporal resolutions. The geospatial accuracy of collected data can range from a few centimeters to several meters, and the use of ground control points (GCPs) is recommended to correct for large geospatial errors. However, whether or not GCPs are used, the true geospatial accuracy of collected UAS data remains unknown. The objective of this study was to measure and compare the geospatial accuracy of images obtained with various UAS platforms at two flight altitudes. Aerial imagery was collected using four platforms equipped with different RGB cameras: Phantom 4, eBee Ag, eBee Plus, and Trimble UX5. All platforms were equipped with manufacturer GPS receivers, and RTK was activated on the eBee Plus. Each platform was flown at 75 and 120 m altitudes, and the experiment was replicated three times. Results demonstrated that using GCPs during data processing improved the horizontal and vertical accuracies of the Phantom 4, eBee Ag, and Trimble UX, decreased the between-flight variability, and accounted for the negative effect of flight altitude. On the other hand, the RTK technology used with the eBee Plus resulted in images with very high geospatial accuracy with or without GCPs. Using GCPs during data processing or RTK technology at the time of flight provided aerial imagery with horizontal accuracies of 1.5 to 10 cm and vertical accuracies of 0.0 to 0.4 m. These results are within an acceptable range for data utilization, unlike the horizontal and vertical accuracies obtained without GCPs or RTK, which ranged from 32 to 441 cm and from 1 to 126 m, respectively. Results from this study quantify the geospatial accuracy of UAS imagery and provide a better understanding of the relationships between the accuracy of the GPS receivers in UAS, flight altitude, and horizontal and vertical accuracies of collected images. Keywords: Accuracy, Drone, Ground control points, Precision agriculture, UAV.

scholarly journals PRECISE AERIAL IMAGE ORIENTATION USING SAR GROUND CONTROL POINTS FOR MAPPING OF URBAN LANDMARKS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 61-66
Author(s):  
F. Kurz ◽  
T. Krauß ◽  
H. Runge ◽  
D. Rosenbaum ◽  
P. d’Angelo
Keyword(s):  
Image Data ◽  
Bundle Adjustment ◽  
Aerial Image ◽  
Ground Control ◽  
Data Sets ◽  
Control Points ◽  
Ground Control Points ◽  
Autonomous Cars ◽  
New Applications ◽  
Image Orientation

<p><strong>Abstract.</strong> Highly precise ground control points, which are globally available, can be derived from the SAR satellite TerraSAR-X. This opens up many new applications like for example the precise aerial image orientation. In this paper, we propose a method for precise aerial image orientation using spaceborne geodetic Synthetic Aperture Radar Ground Control Points (SAR-GCPs). The precisely oriented aerial imagery can then be used e.g. for mapping of urban landmarks, which support the ego-positioning of autonomous cars. The method for precise image orientation was validated based on two aerial image data sets. SAR-GCPs were measured in images, then the image orientation has been improved by a bundle-adjustment. Results based on check points show, that the accuracy of the image orientation is better than 5&amp;thinsp;cm in X and Y coordinates.</p>

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